{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMDB电影评论二分类\n",
    "# 1.load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:31:10.620772Z",
     "start_time": "2020-08-27T09:30:46.812502Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape:(25000,)\n",
      "[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 5952, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32]\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import imdb\n",
    "# num_words is the top $N normal words\n",
    "(train_data,train_labels),(test_data,test_labels) = imdb.load_data(r'D:/Python/workspace/jupyter/data/imdb.npz', num_words=10000)\n",
    "\n",
    "print('shape:{}'.format(train_data.shape))\n",
    "print(train_data[0])\n",
    "\n",
    "print(train_labels[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.1 show the dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:31:11.263054Z",
     "start_time": "2020-08-27T09:31:10.622767Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 'the'), (2, 'and'), (3, 'a'), (4, 'of'), (5, 'to'), (6, 'is'), (7, 'br'), (8, 'in'), (9, 'it'), (10, 'i')]\n"
     ]
    }
   ],
   "source": [
    "dict_words = imdb.get_word_index(r'D:/Python/workspace/jupyter/data/imdb_word_index.json')\n",
    "dict_words = dict([(int(v),k) for (k,v) in dict_words.items()])\n",
    "dict_words = dict([(k, dict_words[k]) for k in sorted(dict_words.keys())])\n",
    "print(list(dict_words.items())[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.2 show the comment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:31:11.270038Z",
     "start_time": "2020-08-27T09:31:11.265050Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "? this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert ? is an amazing actor and now the same being director ? father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for ? and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also ? to the two little boy's that played the ? of norman and paul they were just brilliant children are often left out of the ? list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\n"
     ]
    }
   ],
   "source": [
    "decoded_review = ' '.join([dict_words.get(i-3, '?') for i in train_data[0]])\n",
    "print(decoded_review)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.3 format the input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:31:11.451177Z",
     "start_time": "2020-08-27T09:31:11.274038Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, [1, 2, 3, 4])\n",
      "(1, [4, 1, 3])\n",
      "(2, [5])\n"
     ]
    }
   ],
   "source": [
    "s1 = [[1,2,3,4],\n",
    "      [4,1,3],\n",
    "      [5]]\n",
    "for i in enumerate(s1):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:31:13.638207Z",
     "start_time": "2020-08-27T09:31:11.452174Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 1. 1. ... 0. 0. 0.]\n",
      "(25000, 10000)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def vectorize_sequences(sequences, dimension=10000):\n",
    "    results = np.zeros((len(sequences), dimension))\n",
    "    for i, sequence in enumerate(sequences):\n",
    "        results[i, sequence] = 1.\n",
    "    return results\n",
    "x_train = vectorize_sequences(train_data)\n",
    "x_test = vectorize_sequences(test_data)\n",
    "\n",
    "print(x_train[0])\n",
    "print(x_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:31:13.646187Z",
     "start_time": "2020-08-27T09:31:13.641204Z"
    }
   },
   "outputs": [],
   "source": [
    "y_train = np.asarray(train_labels).astype('float32')\n",
    "y_test = np.asarray(test_labels).astype('float32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 split the validation dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:31:13.805785Z",
     "start_time": "2020-08-27T09:31:13.648182Z"
    }
   },
   "outputs": [],
   "source": [
    "x_val = x_train[:5000]\n",
    "partial_x_train = x_train[5000:]\n",
    "\n",
    "y_val = y_train[:5000]\n",
    "partial_y_train = y_train[5000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 build model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:31:14.453029Z",
     "start_time": "2020-08-27T09:31:13.809749Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From d:\\programs\\anaconda3\\envs\\tf13-keras\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 16)                160016    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 160,305\n",
      "Trainable params: 160,305\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras import models, layers\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Dense(16, activation='relu', input_shape=(10000,)))\n",
    "model.add(layers.Dense(16, activation='relu'))\n",
    "model.add(layers.Dense(1,activation='sigmoid'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 how dense work?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:31:14.462005Z",
     "start_time": "2020-08-27T09:31:14.455024Z"
    }
   },
   "outputs": [],
   "source": [
    "def show_how_dense_work():\n",
    "    # first parameters is $uints without default value\n",
    "    l_dense = layers.Dense(units=4)\n",
    "    get = input('Input Shape:').strip().split(' ')\n",
    "    print('Output Shape:', l_dense.compute_output_shape(input_shape=get))\n",
    "\n",
    "#show_how_dense_work()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 compile model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:31:14.687402Z",
     "start_time": "2020-08-27T09:31:14.464998Z"
    }
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='rmsprop',\n",
    "             loss='binary_crossentropy',\n",
    "             metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:31:55.702500Z",
     "start_time": "2020-08-27T09:31:14.688399Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From d:\\programs\\anaconda3\\envs\\tf13-keras\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 20000 samples, validate on 5000 samples\n",
      "Epoch 1/10\n",
      "20000/20000 [==============================] - 15s 775us/step - loss: 0.4640 - acc: 0.8148 - val_loss: 0.3397 - val_acc: 0.8742\n",
      "Epoch 2/10\n",
      "20000/20000 [==============================] - 3s 149us/step - loss: 0.2664 - acc: 0.9093 - val_loss: 0.2833 - val_acc: 0.8902\n",
      "Epoch 3/10\n",
      "20000/20000 [==============================] - 3s 136us/step - loss: 0.1997 - acc: 0.9313 - val_loss: 0.2730 - val_acc: 0.8914\n",
      "Epoch 4/10\n",
      "20000/20000 [==============================] - 3s 144us/step - loss: 0.1604 - acc: 0.9461 - val_loss: 0.3139 - val_acc: 0.8774\n",
      "Epoch 5/10\n",
      "20000/20000 [==============================] - 3s 135us/step - loss: 0.1392 - acc: 0.9533 - val_loss: 0.3270 - val_acc: 0.8766\n",
      "Epoch 6/10\n",
      "20000/20000 [==============================] - 3s 136us/step - loss: 0.1141 - acc: 0.9618 - val_loss: 0.3102 - val_acc: 0.8880\n",
      "Epoch 7/10\n",
      "20000/20000 [==============================] - 3s 136us/step - loss: 0.0994 - acc: 0.9675 - val_loss: 0.3251 - val_acc: 0.8862\n",
      "Epoch 8/10\n",
      "20000/20000 [==============================] - 3s 136us/step - loss: 0.0847 - acc: 0.9732 - val_loss: 0.4052 - val_acc: 0.8726\n",
      "Epoch 9/10\n",
      "20000/20000 [==============================] - 3s 137us/step - loss: 0.0735 - acc: 0.9774 - val_loss: 0.4098 - val_acc: 0.8700\n",
      "Epoch 10/10\n",
      "20000/20000 [==============================] - 3s 137us/step - loss: 0.0606 - acc: 0.9832 - val_loss: 0.4075 - val_acc: 0.8816\n"
     ]
    }
   ],
   "source": [
    "history_origin = model.fit(partial_x_train,\n",
    "                  partial_y_train,\n",
    "                  epochs=10,\n",
    "                  batch_size=512,\n",
    "                  validation_data=(x_val, y_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 validate model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.1 show the loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:31:55.795251Z",
     "start_time": "2020-08-27T09:31:55.705493Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'val_loss': [0.3397051554679871, 0.2832930678844452, 0.2729761646270752, 0.31394423112869263, 0.32699434170722963, 0.31019408679008487, 0.32510577287673953, 0.4052381679058075, 0.4097917697429657, 0.4075150272846222], 'val_acc': [0.8742000004768372, 0.8901999994277954, 0.8913999997138977, 0.8774000006675721, 0.8765999996185303, 0.888, 0.8862, 0.8726000009536743, 0.8700000003814697, 0.8816000001907348], 'loss': [0.46401018080711365, 0.26636742153167725, 0.19968795590400695, 0.16043492851257324, 0.1391837627887726, 0.11405122776031494, 0.09944477482438087, 0.08471815167665482, 0.07351594853401185, 0.060602583223581315], 'acc': [0.81485, 0.90935, 0.9313, 0.94605, 0.9533, 0.96175, 0.96755, 0.9732, 0.9774, 0.98315]}\n"
     ]
    }
   ],
   "source": [
    "print(history_origin.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:31:58.075374Z",
     "start_time": "2020-08-27T09:31:55.796248Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def show_loss_acc(history, epochs):\n",
    "    %matplotlib inline\n",
    "    plt.figure(figsize=(20,5)) #其中(5, 3)用于控制图片的大小\n",
    "    \n",
    "    lst_loss = history['loss']\n",
    "    lst_valloss = history['val_loss']\n",
    "    lst_acc = history['acc']\n",
    "    lst_valacc = history['val_acc']\n",
    "    \n",
    "    epochs = range(1, epochs + 1)\n",
    "    \n",
    "    plt.subplot(1,2,1)\n",
    "    plt.plot(epochs, lst_loss, 'bo', label='Training loss') #'bo': blue solid pointer\n",
    "    plt.plot(epochs, lst_valloss, 'r', label='Validation loss') # 'b': blue line\n",
    "    plt.title('Training and validation loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.plot(epochs, lst_acc, 'bo', label='Training acc') #'ro': red solid pointer\n",
    "    plt.plot(epochs, lst_valacc, 'r', label='Validation acc') # 'r': red line\n",
    "    plt.title('Training and validation Accuracy')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "show_loss_acc(history_origin.history, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 rebuild a large model and train with 4 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:31:58.393650Z",
     "start_time": "2020-08-27T09:31:58.077334Z"
    }
   },
   "outputs": [],
   "source": [
    "from keras import losses, optimizers\n",
    "model = models.Sequential()\n",
    "l_input = layers.Input((10000,))\n",
    "\n",
    "# a resnet network structure\n",
    "def skip(inputs, units):\n",
    "    d0 = layers.Dense(units, activation='tanh')(inputs)\n",
    "    d1 = layers.Dense(units*2, activation='tanh')(d0)\n",
    "\n",
    "    # skip connection\n",
    "    d_connect = layers.Dense(units*2, activation='tanh')(inputs)\n",
    "    return layers.Concatenate()([d1, d_connect])\n",
    "\n",
    "s1 = layers.Dense(16)(l_input)\n",
    "for i in range(3):\n",
    "    s0 = skip(inputs=s1, units=16)\n",
    "    s1 = skip(inputs=s0, units=16*4)\n",
    "\n",
    "d3 = layers.Dense(128, activation='tanh')(s1)\n",
    "output = layers.Dense(1, activation='sigmoid')(d3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:32:31.661047Z",
     "start_time": "2020-08-27T09:31:58.396642Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 10000)        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 16)           160016      input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 16)           272         dense_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_6 (Dense)                 (None, 32)           544         dense_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_7 (Dense)                 (None, 32)           544         dense_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 64)           0           dense_6[0][0]                    \n",
      "                                                                 dense_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_8 (Dense)                 (None, 64)           4160        concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_9 (Dense)                 (None, 128)          8320        dense_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_10 (Dense)                (None, 128)          8320        concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 256)          0           dense_9[0][0]                    \n",
      "                                                                 dense_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_11 (Dense)                (None, 16)           4112        concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_12 (Dense)                (None, 32)           544         dense_11[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_13 (Dense)                (None, 32)           8224        concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 64)           0           dense_12[0][0]                   \n",
      "                                                                 dense_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_14 (Dense)                (None, 64)           4160        concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_15 (Dense)                (None, 128)          8320        dense_14[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_16 (Dense)                (None, 128)          8320        concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 256)          0           dense_15[0][0]                   \n",
      "                                                                 dense_16[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_17 (Dense)                (None, 16)           4112        concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_18 (Dense)                (None, 32)           544         dense_17[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_19 (Dense)                (None, 32)           8224        concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_5 (Concatenate)     (None, 64)           0           dense_18[0][0]                   \n",
      "                                                                 dense_19[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_20 (Dense)                (None, 64)           4160        concatenate_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_21 (Dense)                (None, 128)          8320        dense_20[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_22 (Dense)                (None, 128)          8320        concatenate_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_6 (Concatenate)     (None, 256)          0           dense_21[0][0]                   \n",
      "                                                                 dense_22[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_23 (Dense)                (None, 128)          32896       concatenate_6[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_24 (Dense)                (None, 1)            129         dense_23[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 282,561\n",
      "Trainable params: 282,561\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "Train on 20000 samples, validate on 5000 samples\n",
      "Epoch 1/10\n",
      "20000/20000 [==============================] - 4s 201us/step - loss: 0.5811 - acc: 0.6954 - val_loss: 0.3388 - val_acc: 0.8650\n",
      "Epoch 2/10\n",
      "20000/20000 [==============================] - 3s 143us/step - loss: 0.2839 - acc: 0.8881 - val_loss: 0.3146 - val_acc: 0.8830\n",
      "Epoch 3/10\n",
      "20000/20000 [==============================] - 3s 142us/step - loss: 0.2302 - acc: 0.9100 - val_loss: 0.2937 - val_acc: 0.8842\n",
      "Epoch 4/10\n",
      "20000/20000 [==============================] - 3s 143us/step - loss: 0.1791 - acc: 0.9330 - val_loss: 0.2885 - val_acc: 0.8918\n",
      "Epoch 5/10\n",
      "20000/20000 [==============================] - 3s 165us/step - loss: 0.1489 - acc: 0.9461 - val_loss: 0.4821 - val_acc: 0.8724\n",
      "Epoch 6/10\n",
      "20000/20000 [==============================] - 3s 147us/step - loss: 0.1378 - acc: 0.9502 - val_loss: 0.3414 - val_acc: 0.8766 - loss: \n",
      "Epoch 7/10\n",
      "20000/20000 [==============================] - 3s 165us/step - loss: 0.1182 - acc: 0.9580 - val_loss: 0.3615 - val_acc: 0.8684\n",
      "Epoch 8/10\n",
      "20000/20000 [==============================] - 3s 169us/step - loss: 0.1080 - acc: 0.9616 - val_loss: 0.4204 - val_acc: 0.8786\n",
      "Epoch 9/10\n",
      "20000/20000 [==============================] - 3s 170us/step - loss: 0.0940 - acc: 0.9668 - val_loss: 0.3399 - val_acc: 0.8690\n",
      "Epoch 10/10\n",
      "20000/20000 [==============================] - 3s 158us/step - loss: 0.0783 - acc: 0.9726 - val_loss: 0.3519 - val_acc: 0.8806\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Model\n",
    "model = Model(inputs=l_input, outputs=output)\n",
    "model.compile(optimizer='rmsprop',\n",
    "             loss='binary_crossentropy',\n",
    "             metrics=['accuracy'])\n",
    "model.summary()\n",
    "history_large = model.fit(partial_x_train,\n",
    "                  partial_y_train,\n",
    "                  epochs=10,\n",
    "                  batch_size=512,\n",
    "                  validation_data=(x_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:32:31.951273Z",
     "start_time": "2020-08-27T09:32:31.663044Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_loss_acc(history_large.history, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 predict some comment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:32:32.458586Z",
     "start_time": "2020-08-27T09:32:31.954263Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "? this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert ? is an amazing actor and now the same being director ? father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for ? and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also ? to the two little boy's that played the ? of norman and paul they were just brilliant children are often left out of the ? list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\n",
      "Objective\n"
     ]
    }
   ],
   "source": [
    "pre = model.predict(x_val[:1])\n",
    "\n",
    "print(' '.join([dict_words.get(i-3,'?') for i in train_data[0]]))\n",
    "print('Objective' if pre[0]>0.5 else 'Negative')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 build a smaller model and show "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:33:00.913880Z",
     "start_time": "2020-08-27T09:32:32.460581Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_25 (Dense)             (None, 4)                 40004     \n",
      "_________________________________________________________________\n",
      "dense_26 (Dense)             (None, 4)                 20        \n",
      "_________________________________________________________________\n",
      "dense_27 (Dense)             (None, 1)                 5         \n",
      "=================================================================\n",
      "Total params: 40,029\n",
      "Trainable params: 40,029\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 20000 samples, validate on 5000 samples\n",
      "Epoch 1/10\n",
      "20000/20000 [==============================] - 3s 168us/step - loss: 0.6089 - acc: 0.6556 - val_loss: 0.5650 - val_acc: 0.7912\n",
      "Epoch 2/10\n",
      "20000/20000 [==============================] - 3s 149us/step - loss: 0.5203 - acc: 0.8056 - val_loss: 0.5164 - val_acc: 0.8434\n",
      "Epoch 3/10\n",
      "20000/20000 [==============================] - 3s 135us/step - loss: 0.4725 - acc: 0.8616 - val_loss: 0.4889 - val_acc: 0.8334\n",
      "Epoch 4/10\n",
      "20000/20000 [==============================] - 3s 136us/step - loss: 0.4384 - acc: 0.8919 - val_loss: 0.4868 - val_acc: 0.8092\n",
      "Epoch 5/10\n",
      "20000/20000 [==============================] - 3s 136us/step - loss: 0.4111 - acc: 0.9109 - val_loss: 0.4501 - val_acc: 0.8818\n",
      "Epoch 6/10\n",
      "20000/20000 [==============================] - 3s 140us/step - loss: 0.3885 - acc: 0.9287 - val_loss: 0.4492 - val_acc: 0.8626\n",
      "Epoch 7/10\n",
      "20000/20000 [==============================] - 3s 135us/step - loss: 0.3687 - acc: 0.9382 - val_loss: 0.4355 - val_acc: 0.8758\n",
      "Epoch 8/10\n",
      "20000/20000 [==============================] - 3s 135us/step - loss: 0.3512 - acc: 0.9465 - val_loss: 0.4400 - val_acc: 0.8668\n",
      "Epoch 9/10\n",
      "20000/20000 [==============================] - 3s 136us/step - loss: 0.3349 - acc: 0.9538 - val_loss: 0.4653 - val_acc: 0.8506\n",
      "Epoch 10/10\n",
      "20000/20000 [==============================] - 3s 136us/step - loss: 0.3207 - acc: 0.9589 - val_loss: 0.4148 - val_acc: 0.8884\n"
     ]
    }
   ],
   "source": [
    "model = models.Sequential()\n",
    "model.add(layers.Dense(4, activation='relu', input_shape=(10000, )))\n",
    "model.add(layers.Dense(4, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(optimizer='rmsprop',\n",
    "             loss='binary_crossentropy',\n",
    "             metrics=['accuracy'])\n",
    "model.summary()\n",
    "history_small = model.fit(partial_x_train,\n",
    "                  partial_y_train,\n",
    "                  epochs=10,\n",
    "                  batch_size=512,\n",
    "                  validation_data=(x_val, y_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 show the loss and acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-27T09:33:01.194131Z",
     "start_time": "2020-08-27T09:33:00.915876Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import  matplotlib.pyplot as plt\n",
    "x = range(1, 11)\n",
    "plt.figure('differences', figsize=(20, 5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(x, history_origin.history['val_loss'], label='origin')\n",
    "plt.plot(x, history_large.history['val_loss'], label='large')\n",
    "plt.plot(x, history_small.history['val_loss'], label='small')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(x, history_origin.history['acc'], label='origin')\n",
    "plt.plot(x, history_large.history['acc'], label='large')\n",
    "plt.plot(x, history_small.history['acc'], label='small')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
